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Generative AI courses

608 Courses

California Institute of Technology Coursera

Gemini in Google Drive - 日本語版

Gemini in Google Drive - 日本語版 Gemini for Google Workspace は、生成 AI 機能へのアクセスをユーザーに提供するアドオンです。動画レッスン、ハンズオンアクティビティ、実用的な例を使用して、Gemini in Google ドライブの機能について詳しく説明します。このコースを修了すると、自信を持って Gemini in Google ドライブを活用し、ワークフローを改善する.
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California Institute of Technology Coursera

Gemini in Google Drive - 한국어

Gemini in Google Drive - 한국어 University: 제공: Coursera Categories: Generative AI Courses Google Workspace Courses Gemini Courses Google Workspace를 위한 Gemini는 사용자에게 생성형 AI 기능에 대한 액세스를 제공하는 부가기능입니다. 이 과정은 동영상 강의, 실습, 실제 사례를 사용하여 Google Drive의 Gemini가 제공하는 기능.
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California Institute of Technology Coursera

GenAI for Sales Operations Specialists

GenAI for Sales Operations Specialists This course introduces Sales Operations Specialists to the transformative capabilities of Generative Artificial Intelligence (GenAI). Participants will explore practical strategies to leverage GenAI in various sales operations tasks, enhancing efficiency, productivity, and strategic insights. Through a com.
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California Institute of Technology University of Naples Federico II

Developing Explainable AI (XAI)

Developing Explainable AI (XAI) As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course pro.
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California Institute of Technology Coursera

Smarter Content Creation with Microsoft Copilot

Smarter Content Creation with Microsoft Copilot This course is the third in a series designed to revolutionize your marketing capabilities with Microsoft Copilot's GenAI. Focusing on content creation, you’ll explore the diverse types of content you can create using Microsoft Copilot. Next, you’ll learn how to brainstorm content ideas and effecti.
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California Institute of Technology Coursera

Upgrade Your Marketing Strategy with Microsoft Copilot

Upgrade Your Marketing Strategy with Microsoft Copilot This course is primarily aimed at first- and second-year undergraduates interested in engineering or science, as well as high school students and professionals with an interest in programming. "Upgrade Your Marketing Strategy with Microsoft Copilot" is the first in a series of co.
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California Institute of Technology Coursera

Using Microsoft Copilot to Understand Your Customer

Using Microsoft Copilot to Understand Your Customer Description: This course is the second in a series that aims to transform your marketing capabilities with Microsoft Copilot's GenAI. Its focus is on understanding your customers. In this course, you’ll learn how to train Microsoft Copilot with marketing-related data. You’ll then le.
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California Institute of Technology Coursera

Work with Gemini Models in BigQuery - 日本語版

Gemini モデルを使用した BigQuery 作業 - 日本語版 このコースでは、BigQueryで生成型AI作業にAI/MLモデルを使用する方法を紹介します。顧客関係管理に関連する実際の使用事例を通じて、Geminiモデルを使用してビジネス問題を解決するワークフローを解説します。理解を深めるために、SQLクエリとPythonノートブックを使用したコードソリューションを詳細に案内します。
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California Institute of Technology Coursera

Work with Gemini Models in BigQuery - 简体中文

Work with Gemini Models in BigQuery - 简体中文 이 과정은 BigQuery에서 생성형 AI 작업에 AI/ML 모델을 사용하는 방법을 보여줍니다. 고객 관계 관리와 관련된 실제 사용 사례를 통해 Gemini 모델로 비즈니스 문제를 해결하는 워크플로를 설명합니다. 이해를 돕기 위해 SQL 쿼리와 Python 노트북을 사용하는 코딩 솔루션을 단계별로 안내합니다. University: Provider:.
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California Institute of Technology Coursera

Gemini in Google Drive - Bahasa Indonesia

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A generative ai course is a fast-growing field of machine learning that can create new content, translate languages, write different types of creative content, and answer your questions in an informative way. It has great potential to revolutionize the way we create and use products.

A generative ai course refers to any artificial intelligence model that generates new data, information, or documents.

For example, many companies record their meetings, both live and virtual. Here are a few ways generative AI could transform these recordings:

And this is only a small part of all processes.

Generative AI Model Examples

There are a number of products using generative ai courses already available on the market – we'll give you a few examples below. The underlying principle of the generative ai courses at AI Eeducation varies depending on the specific model or algorithm used, but some common approaches include:

  1. Variational Autoencoders (VAEs) are a type of generative model that learns to encode input data into a latent space and then decode it back into the original data. The "variational" part of the name refers to the probabilistic nature of the latent space, allowing the model to generate a variety of outputs.

  2. Generative Adversarial Networks (GaN): GaNs consist of two neural networks, a generator and a discriminator, that are trained simultaneously through adversarial learning. The generator creates new data, and the discriminator evaluates how well the generated data matches the real data. The competition between the two networks causes the generator to improve over time in producing realistic outputs.

  3. Recurrent Neural Networks (RNNS) and Long Short-Term Memory (LSTM): These types of neural networks are often used to generate sequences such as text or music. RNNS and LSTM have memory that allows them to process a series of events over time, making them suitable for tasks where the order of elements is important.

  4. Transformer models: Transformer models, especially those with attention mechanisms, are very successful in various generative tasks. They can remember long-term dependencies and relationships in data, making them effective for tasks such as language translation and text generation.

  5. Autoencoders: Autoencoders consist of an encoder and a decoder, and they are trained to reconstruct the input data. Although they are primarily used for learning to represent and compress data, variations such as denoising autoencoders (e.g. in images) can be used for generative tasks.

An ai generative course involves feeding a model a large data set and optimizing its parameters to minimize the difference between the generated output and the real information. A model's ability to produce realistic and rich content depends on the complexity of its architecture, the quality and quantity of training data, and the optimization techniques used during training!